title: BNFinder2: Faster Bayesian network learning and Bayesian classification.
abstract: CONCLUSIONS
  Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores.
  
  
  METHODS
  BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user's manual, introductory tutorial and supplementary methods.
authors.affiliation: Institute of Informatics, University of Warsaw, Warsaw, Poland. dojer@mimuw.edu.pl
authors.forename: Norbert
authors.initials: N
authors.lastname: Dojer
authors.forename: Pawel
authors.initials: P
authors.lastname: Bednarz
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Podsiadlo
authors.forename: Bartek
authors.initials: B
authors.lastname: Wilczynski
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015233:Models, Statistical; D012372:ROC Curve; D012984:Software